# Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization

arXiv: https://arxiv.org/abs/2503.23370 · HTML: https://mlsanity.com/paper/2503.23370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [TSIT: A Simple and Versatile Framework for Image-to-Image Translation](https://mlsanity.com/paper/2007.12072/) (2020)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models](https://mlsanity.com/paper/2304.10597/) (2023)
- [Training a Task-Specific Image Reconstruction Loss](https://mlsanity.com/paper/2103.14616/) (2021)
- [Generic Perceptual Loss for Modeling Structured Output Dependencies](https://mlsanity.com/paper/2103.10571/) (2021)
- [DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories](https://mlsanity.com/paper/2002.06832/) (2020)
- [Look ATME: The Discriminator Mean Entropy Needs Attention](https://mlsanity.com/paper/2304.09024/) (2023)
- [Multi-Domain Image-to-Image Translation with Adaptive Inference Graph](https://mlsanity.com/paper/2101.03806/) (2021)
- [Bridging Scales in Map Generation: A scale-aware cascaded generative mapping framework for seamless and consistent multi-scale cartographic representation](https://mlsanity.com/paper/2502.04991/) (2025)

## Abstract (arXiv)

In intelligent cartographic generation tasks empowered by generative models, the authenticity of synthesized maps constitutes a critical determinant. Concurrently, the selection of appropriate evaluation metrics to quantify map authenticity emerges as a pivotal research challenge. Current methodologies predominantly adopt computer vision-based image assessment metrics to compute discrepancies between generated and reference maps. However, conventional visual similarity metrics-including L1, L2, SSIM, and FID-primarily operate at pixel-level comparisons, inadequately capturing cartographic global features and spatial correlations, consequently inducing semantic-structural artifacts in generated outputs. This study introduces a novel Map Feature Perception Metric designed to evaluate global characteristics and spatial congruence between synthesized and target maps. Diverging from pixel-wise metrics, our approach extracts elemental-level deep features that comprehensively encode cartographic structural integrity and topological relationships. Experimental validation demonstrates MFP's superior capability in evaluating cartographic semantic features, with classification-enhanced implementations outperforming conventional loss functions across diverse generative frameworks. When employed as optimization objectives, our metric achieves performance gains ranging from 2% to 50% across multiple benchmarks compared to traditional L1, L2, and SSIM baselines. This investigation concludes that explicit consideration of cartographic global attributes and spatial coherence substantially enhances generative model optimization, thereby significantly improving the geographical plausibility of synthesized maps.